NIST Fingerprint Image Quality Page 1 08/19/04

Fingerprint Image Quality

Elham Tabassi

Charles L. Wilson

Craig I. Watson

NISTIR 7151

August 2004
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ABSTRACT ................................................................................................................................... 3

1 INTRODUCTION.................................................................................................................... 3

1.1 TERMINOLOGY....................................................................................................................... 4

1.2 DATA SET AND MATCHER ALGORITHM.................................................................................. 4

2 PREVIOUS WORK ................................................................................................................. 6

3 DEFINING FINGERPRINT IMAGE QUALITY................................................................. 7

3.1 IMAGE QUALITY AS CLASSIFICATION ................................................................................. 11

4 MEASURING FINGERPRINT IMAGE QUALITY.......................................................... 12

4.1 FEATURE EXTRACTION ........................................................................................................ 13

4.1.1 MINDTCT input ........................................................................................................... 13

4.1.2 Generate image quality map ........................................................................................ 13

4.1.4 Feature vectors............................................................................................................. 16

4.2 PATTERN RECOGNITION (REGRESSION VS. CLASSIFICATION)............................................. 28

4.2.1 Training the neural network......................................................................................... 34

5 ASSESSING FINGERPRINT IMAGE QUALITY ............................................................ 37

6 CONCLUSION....................................................................................................................... 69

REFERENCES ............................................................................................................................ 71

APPENDIX A .............................................................................................................................. 72

APPENDIX B............................................................................................................................... 72
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Abstract

In this report, we propose a new definition of quality of fingerprint impressions and present

detailed algorithms to measure image quality for fingerprints. We define fingerprint image

quality as a predictor of matcher performance before a matcher algorithm is applied. This means

presenting the matcher with good quality fingerprint images will result in high matcher

performance, and vice versa, the matcher will perform poorly for poor quality fingerprints. We

also have carried out an objective evaluation of the quality assessment of fingerprint images.

Our quality measure is implemented in the C programming language and has been tested on 20

different live scan and paper fingerprints datasets collected in different operational settings. Our

implementation is publicly, but export controlled, available as part of NIST’s fingerprint

software distribution.

1 Introduction

Studies carried out by NIST as part of its Patriot Act mandate have demonstrated that all

fingerprint systems tested have widely varying performance which depends, among other

factors, on fingerprint image quality [21, 22, 5, 6]. In this paper we will define a novel measure

of fingerprint image quality, which can be used to estimate fingerprint match performance. We

will present test results for this image quality measure for 20 different live scan and scanned

paper fingerprint data sets. These datasets are explained in section 1.2. The ability to predict

matcher performance has been evaluated for 14 different fingerprint systems supplied by 8

commercial fingerprint vendors. An explanation of the NIST Fingerprint SDK test plus more

detailed explanation of datasets are in [6].

An ideally sensed or scanned fingerprint image has clear and distinct ridges and valleys. An

automatic fingerprint recognition system can perform well on such fingerprint images.

However, precise fingerprint acquisition has some peculiar and challenging aspects [1]. Often

skin condition or imperfect acquisitions cause the captured fingerprint image to be far from

ideal. Unclean sensor plates, non-uniform and inconsistent contact [2] can result in poor

samples and feature extraction artifacts during image processing and hence increase false

accept/reject rate.

An automatic fingerprint recognition system has traditionally consisted of three subsystems:

fingerprint image acquisition, feature extraction, and matching. In image acquisition, a digital

image of a fingerprint is captured either from the live scan of a person’s finger or from an inked

impression of a person’s finger on paper (e.g. fingerprint cards). Feature extraction is the

process of representing the captured image in some space to facilitate matching. Matching

involves computing the likelihood of the fingerprint coming from subjects (persons) in the

database. The performance of the whole system depends on how well each subset behaves.

It is desirable to assess “quality” of a fingerprint before any matching process. This allows poor

image acquisition to be corrected before poor quality is entered into users’ databases. Higher

matcher performance can be achieved if a fingerprint’s quality (in the sense that will be defined

later in this paper) is sufficiently good and overall database integrity is improved.
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This motivates us to define fingerprint image quality as a predictor of a matcher’s performance.

This means presenting the matcher with good quality fingerprint images will result in high

matcher performance, and vice versa, the matcher will perform poorly for poor quality

fingerprints. Despite some on-going and past effort in the investigation of fingerprint image

quality [2,11,12,13,14,16], to our knowledge, nobody has publicly defined fingerprint image

quality as a scalar to predict matcher performance. Predicting matcher performance is also

valuable for biometric fusion of multiple fingerprints because the fingerprints with the best

image quality can be assigned higher weight in the fusion.

The rest of this paper is organized as follows: we briefly discuss the framework and datasets

used for experiments. We briefly survey some on-going and past effort in fingerprint image

quality research in section 2. Section 3 defines fingerprint image quality in a broad general

sense without addressing implementation details. Although it was defined and implemented for

fingerprints, we believe this definition of quality can and should be applied for other biometric

modalities (e.g. face, iris) if proper feature vectors are defined and measured. Section 4 covers

our implementation for measuring fingerprint quality in detail. Section 5 discusses how we test

our quality system. Section 6 summarizes our findings. Appendix A shows the image quality

predictions for this method over all 20 data sets and all 14 fingerprint-matching systems.

1.1 Terminology

A biometric sample is an abstraction of the material input to a recognition system: it may be an

image (fingerprint, face, iris), speech sequence(s) or some arbitrary composite. We define a

gallery Γ, to represent the set of enrollees in a biometric system. It contains identically one

biometric sample per subject. We use G = | Γ| to denote the size of the enrolled population.

Likewise a probe set, ΠΓ, serves as the set of legitimate users of the system. A match describes

the comparison of a probe and a gallery sample of the same individual. A non-match arises from

samples of different persons.

1.2 Data set and matcher algorithm

NIST has acquired a collection of live-scan and scanned paper fingerprints datasets collected at

different operational settings.

Department of State (DOS-C) and Department of Homeland Security (DHS2-C) datasets

contain live-scan plain impressions of the left and right index fingers. They were captured in an

operational environment and should give results of what to expect from real time data of plainto-plain (p2p) impression matching.

Department of Homeland Security Ten-print (DHS10) and Texas Department of Public Safety

(TXDPS) were mainly inked paper segmented plain and rolled impressions and Department of

Homeland Security Benefits (BEN) was mainly live-scan segmented plain and rolled

impressions. These three datasets are most useful in evaluating plain to rolled (p2r) impression
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matching. DHS10 and TXDPS are examples of the currently available fingerprint databases

and BEN represents data captured with newer live-scan methods with some control on quality.

US-VISIT Point of Entry and Bio-Visa Application data (VISIT_POE, VISIT_POE_BVA)

contain live-scan plain impressions of the left and right index fingers. They are captured in an

operational environment and represent the current image data being captured in the US-VISIT

program. The BVA data was captured at State Department Consular offices when applicants

apply for visas and the POE are captured by the Department of Homeland Security at the points

of entry into the US for international travelers. VISIT_POE compares two instances of a person

that were captured at the POE and VISIT_POE_BVA compares a POE image to a BVA image.

A random sample of 6,000 subjects was selected from each of the datasets. Since DHS2-C was

consolidated after testing had started, the final dataset size after consolidation was only 5,888

not 6,000. The process of consolidation has been explained in detail in [22]. The four-finger

slaps in BEN, DHS10, and TXDPS were all segmented with the NIST segmentor as discussed

in [10].

NIST has conducted testing of one-to-one SDK (Software Development Kit) based COTS

fingerprint matching systems to evaluate the accuracy of one-to-one matching [6]. Eight

commercial fingerprint vendors submitted their systems for evaluation, some more than one

system. A total of fourteen SDK based matching application were tested on twenty different

single finger datasets of varying difficulty. We have used similarity scores of SDK vendors to

develop and test our image quality assessment system. In this report, we refer to SDK vendors

by their SDK abbreviated letters. Table 1 shows list of vendor names associated with SDK

letters.
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SDK LETTER VENDOR NAME

A Name Not Released

B Name Not Released

C NEC

D Cogent Systems, Inc.

E Name Not Released

F Cogent Systems, Inc.

G SAGEM Morpho, Inc.

H NEC

I Cogent Systems, Inc.

J SAGEM Morpho, Inc

K Neurotechnologija Ltd.

L Name Not Released

VTB NIST

Table 1. List of SDK vendor names associated with SDK letters1

2 Previous Work

Many papers in biometric literature address the problem of assessing fingerprint image quality.

Nill & Bouzas [11] propose an objective image quality based on the digital image power of

normally acquires scenes. Their system is designed to assess the quality of digital images and

can be applied to fingerprint as well. Bolle, et al [12] used ratio of directional area to other nondirectional area as a quality measure. Shen et al [13] applied gabor filter to image sub-blocks, to

identify blocks with clear repetition of ridge and valley pattern as good quality blocks. Both

[12] and [13] only use the local orientation information and neglect information on global

uniformity and continuity The configurations of ridges and valleys within a local neighborhood

vary with the quality of input fingerprint images, so a well defined sinusoidal shaped waves of

ridges and valleys may not always be observed. Global features are needed for a more precise

region mask classification. They both use subjective method to test the performance of their

proposed quality analysis algorithms.

Ratha and Bolle [14] proposed a method for image quality estimation from wavelet compressed

fingerprint image, where the fingerprint image is WSQ compressed. Still evaluating their

quality measure is a subjective matter.

 

1

 These tests were performed for the Department of Homeland Security in accordance with section 303 of the Border

Security Act, codified at 8 U.S.C. 1732. Specific hardware and software products identified in this report were used

in order to adequately support the development of technology to conduct the performance evaluations described in

this document. In no case does such identification imply recommendation or endorsement by the National Institute

of Standards and Technology, nor does it imply that the products and equipment identified are necessarily the best

available for the purpose.
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Hong et al [15] modeled the ridge and valley pattern as sine wave, and compute the amplitude,

frequency as well as the variance of the sine wave to decide the quality of the fingerprint. They

classify regions of fingerprint as recoverable or unrecoverable. If the percentages of recoverable

blocks are smaller than a specified threshold, the input fingerprint is rejected as poor quality.

Lim and Yau [16] propose algorithms for estimating quality and validity of a fingerprint image

in spatial domain. They verify the repetition of ridge and valley patterns by the ratio of the

eigenvalues obtained from covariance matrix for the image block’s grey-level gradient. They

use the quality of the feature extracted from the fingerprint image by the automatic fingerprint

identification system as the benchmark to test the performance of their proposed image quality

analysis procedure. Their system is aimed to flag poor quality or invalid fingerprint images.

Yao et al [2] compute quality Q as a measure of how much reliable directional information is

available in a fingerprint image. They have used ROC curves to show better performance is

achieved but did not use ROC to test the performance of their proposed quality analysis

algorithms.

To our knowledge, nobody has publicly defined biometric sample quality as a predictor for

recognition performance, and nobody has used ranked ROCs to assess the measured quality.

3 Defining Fingerprint Image Quality

A fingerprint is a pattern of friction ridges on the surface of a fingertip. A good quality

fingerprint has distinguishable patterns and features that allow the extraction of features that are

useful for subsequent matching of fingerprint pairs. A minutia based automatic fingerprintmatching algorithm uses features that compares local ridge characteristics (minutia) of two

fingerprints (biometric samples) g (i) x and p( j) x and produces a real valued similarity score

( , ) ij g (i) p( j) s = F x x (eq. 1)

where subscript g(i) denotes i-th gallery and p( j) denoted j-th probe and ij s is the score of the

i-th gallery matched against the j-th probe.

We call similarity scores ii s of a genuine (i.e. same person) comparisons match scores, and

similarity scores s i j ij , ≠ of imposter (i.e. different person) comparisons non-match scores.

So, for a gallery of size G, with the assumption that there is one and only one biometric sample

for each subject enrolled in the system, for each probe image p(i) x there is one match score ii s

and G-1 non-match scores s i j ji , ≠ . Let ( ) m i s x denotes match score for sample p(i) x and

( ) n ji s x non-match scores of p(i) x and g ( j) x , while i ≠ j .
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A higher similarity score is construed to indicate a higher likelihood that the samples come from

the same individual. Let ( ) m M s denote the cumulative distribution function (CDF) of the

match scores, and ( ) n N s the CDF of non-match scores. The Detection Error Tradeoff

Characteristic (DET) is a plot of the false non-match rate,

( ) m FNMR = M s (eq. 2)

against the false match rate,

1 ( ) n FMR= − N s (eq. 3)

for all values of ms and n s . The DET, and the equivalent ROC, are the commonest statement of

performance of a verification system.

We define fingerprint image quality as a predictor of a matcher’s performance. Before

advancing any further, we need to quantify matcher performance. The similarity score is the

ultimate statement of expected performance: in conjunction with the underlying match and nonmatch distributions it yields likelihood for the samples coming from the same person or

different people. The match and non-match distributions are the result of complex non-linear

algorithms and are not usually random but are strongly dependent on the internal algorithm and

how its parameters are set.

Figure 1 shows the histogram of vendor VTB [10,6] match and non-match scores of fingerprint

impressions of 216 subjects in data set NIST- SD29 [3]. There are 216 match scores and 46440

non-match scores.

4 36 68 100 132 164 196 228

0.00

0.05

0.10

0.15 match scores

non match scores

sd29 - vtb match and non match scores histogram

Figure 1 - SD29 VTB match and non-match scores histogram
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It is common for the match distribution to be wider than non-match distribution. It is also quite

typical for the two distributions to overlap. Overlapping match and non-match distributions

means a given sample i x will match falsely if its match score ( ) m i s x is less than some nonmatch scores s (x ) (s (x ) s (x ) i j) n ji m i < n ji ≠ . If the quality measure q is to be predictive of

matcher performance, good quality fingerprints must be those with high match scores and well

separated from the non-match distribution. Similarly, poor quality fingerprints are those with

lower match scores, in particular those where their match scores are in the region of overlap

with non-match scores.

Therefore, quality measure q should be indicative of the degree to which the match distribution

( ) m M s is separated from the non-match distribution ( ) n N s . Specifically, we define quality qi

of biometric sample i x to predict

∀ ∈ − = i

n ji

m i n ji

i x

s x

s x E s x

o x

( ( ))

( ) [ ( )] ( )

σ

Γ or Π (eq. 4)

where E[.] is mathematical expectation, and σ (.) is standard deviation, ( ) m i s x is the match

score, and ( ) n ji s x are the non-match scores of sample i x , ∀j, i ≠ j . Comparing a probe

sample i x with an internal gallery of G samples, which include one and only one sample from

the same subject (person), results in a vector of G scores, s. Only one element of vector s is i x ’s

match score, and the other G-1 are its non-match scores. E[.] is evaluated by computing mean

of all non-match scores of probe sample i x to all G-1 non-matching gallery entries. Likewise,

σ (.)is standard deviation estimated solely from the non-matching elements of s. We call

( )i o x normalized match score of sample i x . Basically, we are comparing the subject’s

biometric sample to the claimed match sample and to other non-matching samples, and

adjusting the raw score on the basis of the extra scores.

Figure 2 shows box plot of normalized match scores of SDK vendor matcher F as generated

using equation (4) for right index fingers of dataset DOS-C for fingerprints that are matched

correctly and those that are matched incorrectly separately. Note that fingerprints with lower

values of o(.) are matched falsely, where fingerprints that are matched correctly are having

higher values o(.) .
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Figure 2. Box plot o(.) falsely matched and correctly matched separated. The black dot

inside the box represents the median; the box shows inter-quartile-range of normalized

match scores (eq. 4) of vendor F for right index fingerprints in dataset DOS-C. Whiskers

are drawn are 1.5(inter-quartile-range) and data points beyond, which are considered

outliers, are drawn individually.

For each fingerprint, we define its image quality as predictor of its normalized match score.

Similarity scores as defined by equation 1, and so normalized match scores as defined by

equation 4, are a function of both probe and gallery samples, but quality as defined here is a

scalar value which is measured for each sample separately. Therefore, pair wise quality q as

defined below, should be predictive of recognition performance of pair ( , ) gallery probe x x .

( , ) gallery probe q = H q q (eq. 5)

Extensive testing at NIST [5, 6] has shown that recognition errors are triggered by low quality

samples. That is, H(.) is simply a min function of the individual numbers probe q , and gallery q ,

and so pair wise quality is defined in equation 6. In this paper quality refers to q as defined in

equation 6.

min( , ) gallery probe q = q q (eq. 6)

In an operational setting, if the enrolled samples are assured to have high quality, then a

measurement of quality of a subject’s biometric sample (probe) can be sufficient to predict its

normalized match score.
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We measure (scalar value) quality qi for biometric sample i x , by first computing a feature

vector vi, which contains appropriate signal or image fidelity characteristics of i x and then

finding some (nonlinear) mapping from vi to ( )i o x . Mathematically speaking:

vi ( )i = L x (eq.7)

q o x I i = ( i ) = ~ (vi) (eq. 8)

Function L(.) will be realized by computing characteristics and features of i x that convey

information for a matching algorithm. Applying L(.) to a sample i x results in an n-dimensional

feature vector vi. For fingerprints, this includes measured clarity of ridges and valleys, size of

the image, and measures of number and quality of minutiae. Section 4 has a detailed

explanation of our feature vectors.

Function I(.) is a mapping from the space of feature vectors v to normalized match scores

o(.) . ( ) ~ i o x is the predicted value for ( )i o x .

3.1 Image Quality as Classification

We defined biometric sample quality as a predictor of its normalized match score as defined in

equation 4. Equation 8 suggests use of various regression methods to estimate the response

function o(.) from a vector of variables v. However, as shown in detail in section 4, we tried

various regression methods and failed to find a good fit primarily because: sample-specific

measures (components of vector v) are not linearly related to the response variable o(.) and so

nonlinear functions have to be found; outliers heavily influence data; and residual errors are not

Gaussian for any of the regression methods tried. Plus, it is sufficient to know the level of

quality (e.g. high, good, medium, low) and since quality is defined as predictor of

o(.) (normalized match scores) it is sufficient to know the range of ( )i o x (e.g. high, good,

medium, low) rather than its exact value for each sample i x . These facts lead us to restate the

problem in terms of classification. This means we define sample quality as a measure that

predicts the bin of ( )i o x . Now function I(.) from equation 8 is basically a classifier that maps

feature vector vi of sample i x (eq. 7) to a quality number Q such that ( )i o x falls in the k-th bin

where, without loss of generality, Q = k. That is, the quality number qi of sample i x is the bin in

which ( )i o x will fall. Our implementation employs an artificial neural network [17] as the

classifier.

The number of allowed bins, K, constitutes a coarseness parameter, against which the quality

number can be traded off. For example, a high / low level (K = 2) is easier to achieve than the

continuous case as K tends to infinity discussed above. In this paper we chose K = 5, that is we

have five levels of quality: poor (5), fair (4), good (3), very good (2), and excellent (1). The
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choice of 5 levels of image quality is a compromise. Studies at NIST [6] shows that eight to ten

levels would be needed to fully characterize a matcher that is very sensitive to image quality.

Matchers that are insensitive to image quality could be well characterized by three levels of

image quality. We have selected five levels, which is the correct level for a medium to good

quality matcher. We used irregularly spaced quantiles of o(.) to define the bin boundaries for

o(.) . For a sample, x , the quantile function is the inverse of the empirical CDF; that is,

quantile(p) = min{z | proportion p of o(x) χ z}

We define o(.) bin boundaries as shown in Table 2, where W (.) denotes the CDF of samples

that are falsely (wrongly) matched and C denotes CDF of samples that are correctly matched.

Q QUALITY RANGE

5 poor [0, W-1(0.75)]

4 fair (C-1(0.75)], C-1(0.05)]

3 good (C-1(0.05), C-1(0.2)]

2 very good (C-1(0.2), C-1(0.6)]

1 excellent (C-1(0.6), C-1(1)]

Table 2. Bin boundary for normalized match scores o(.). The boundaries were set by

inspection to give useful categorization of the normalized match scores statistic.

Our bin boundary selection agrees with our definition of quality; we are labeling samples with

poorest recognition rate as poor, (in this case all of them are falsely matched). Samples with fair

quality are those that are borderline, some of the these samples are falsely matched, most of

good quality samples are matched correctly and very good and excellent are those samples that

almost all are matched correctly. In other words, excellent quality samples are those with very

high FNMR (eq 2) and poor quality samples are those with high FMR (eq 3).

4 Measuring Fingerprint Image Quality

In this section we discuss our implementation of L(.) and I(.) of equation 7 and 8 for

fingerprint images. We first apply L(.) to a biometric sample i x to get feature vector vi and

then use vi as input into a neural network, I(.) . L(.) is realized by computing characteristics

and features of biometric sample i x that convey information for a matching algorithm.
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4.1 Feature extraction

This section explains feature extraction for fingerprints. Our proposed definition and

measurement of biometric sample quality can be applied to other biometric modalities if the

appropriate feature vectors are defined and computed accordingly.

It is known that fingerprint matcher algorithms commonly in use are sensitive to clarity of

ridges and valleys, measures of number and quality of minutiae, and size of the image. We

have used NIST Fingerprint Image Software (NFIS) [7] package to extract features, i.e.

implementation of L(.) of equation 7. The MINDTCT package of NFIS has a fingerprint

minutia detector algorithm that accepts a fingerprint image and automatically detects minutia. It

also assesses minutia quality and generates an image quality map. To locally analyze a

fingerprint image, NFIS divides the image into grids of blocks. To assess the quality of each

block, NFIS computes several maps (direction map, low contrast, low flow, and high curve) and

summarizes the result in a quality map. All pixels in a block are assigned the same result. It

should be noted that the NFIS algorithms and software parameters have been designed and set

to process images scanned at 500 pixels per inch (19.69 pixels per millimeter) and quantized at

256 levels of gray. A discussion of MINDTCT parameters and how it is used in our quality

assessment follows.

4.1.1 MINDTCT input

MINDTCT reads in an ANSI/NIST formatted file and searches the file structure for a grayscale

fingerprint record. The application is capable of processing ANSI/NIST Type-4, Type-13, and

Type-14 fingerprint image records [8].

4.1.2 Generate image quality map

MINDTCT measures quality of localized regions in the image including determining the

directional flow of ridges in the image and detecting regions of low contrast, low ridge flow,

and high curvature. These last three conditions represent unstable areas in the image where

minutiae detection is unreliable, and together they can be used to represent levels of quality in

the image. Each of these characteristics is discussed below.

4.1.2.1 Direction map: The purpose of this map is to represent areas of the image with sufficient

ridge structure. Well-formed and clearly visible ridges are essential to reliably detection of ridge

endings and bifurcations. To locally analyze the fingerprint, the image is divided into grid of

blocks. All pixels within a block are assigned the same value. To minimize the discontinuity in

block values as one move from one block to its neighbor, windows are defined to surround

blocks, and windows overlap from one block to the next. For each block in the image, the

surrounding window is rotated incrementally and a Discrete Fourier Transform (DFT) is

conducted at each orientation. The details are given in [7].

4.1.2.2 Low contrast map: An image map called the low contrast map is computed where the

blocks of sufficiently low contrast are flagged. This map separates the background of the image

from the fingerprint, and maps out smudges and lightly inked areas of the fingerprint. Minutiae

are not detected within low contrast blocks in the image. This software computes the pixel

intensity distribution within the block’s surrounding window. A specified percent (10%) of the
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distribution’s high and low tails are trimmed as possible outliers, and the width of the remaining

distribution is measured. A pixel intensity threshold was derived empirically from a training

sample of low and high contrast blocks extracted from real fingerprint images. Blocks that have

narrow dynamic range in pixel intensity are flagged as low contrast areas.

4.1.2.3 Low flow map: Low flow map marks the blocks that could not initially be assigned a

dominant ridge flow. Minutiae detected in low flow areas are not reliable.

4.1.2.4 High curve map: Minutiae detected in high curvature areas are not reliable. This is

especially true of the core and delta regions of a fingerprint. A high curve map is used to marks

blocks that are in high curvature areas of the fingerprint.

4.1.2.5 Quality map: As discussed, the low contrast map, low flow map, and the high curve map

all point to different low quality regions of the image. The information in these maps is

integrated into one general map, and contains 5 levels of quality (4 being the highest quality and

0 being the lowest). The background has a score of 0, a score of 4 means a very good region of

fingerprint. The quality assigned to a specific block is determined based on its proximity to

blocks flagged in the above-mentioned maps. We display quality map grayscale image with

black, dark gray, medium gray, light gray, and white corresponding to scores of 0 to 4

respectively.

Figure 3 shows an example of a good quality fingerprint and its gray scale image of quality map

as described above. Note that most of gray scale quality map image is white. Figure 4 shows an

example of a poor quality image and its gray scale level translation. It is notable that majority

gray scale quality map image is dark gray or black. Quality maps of both fingerprints are in

appendix B. We have subjectively checked quality map of several fingerprint images and we are

confident that quality maps generated by MINDTCT is a good representation of areas with clear

ridges and valleys and areas where minutiae detection is (or is not) reliable.

For each fingerprint, we used MINDTCT to generate its quality map. Blocks with quality 0 are

regarded as background. We compute the total number of blocks with quality 1 or better as the

effective size of the image or foreground. Then percentage of foreground blocks with quality 1,

2, 3, and 4 are computed. We call them quality zones 1, 2, 3, and 4 respectively. Fingerprint

images with higher number of quality zone 4 (equivalently smaller number of quality zone 1

and/or 2) are more desirable.
NIST Fingerprint Image Quality Page 15 08/19/04

Figure 3. An example of a fingerprint subjectively assessed to be of good quality

Figure 4. An example of a fingerprint subjectively assessed to be of bad quality

4.1.3 Assess Minutia Quality: NFIS computes a quality/reliability to be associated with each

detected minutia point. Although NFIS performs several steps of false minutiae removal, false

minutiae usually remain in the candidate list. A robust quality measure can help manage this in

that false minutiae should be assigned a lower quality than true minutiae. Two factors are

combined to produce a quality measure for each detected minutia point. The first is taken

directly from the location of the minutia point within the quality map described above. The

second factor is based on simple pixel intensity statistics (mean and standard deviation) within

the immediate neighborhood of the minutia point. An area with clear ridges and valleys will

have a significant contrast that will cover the full grayscale spectrum. Consequently, the mean

pixel intensity of the neighborhood will be very close to 127. For similar reasons, the pixel

intensities of an ideal neighborhood will have a standard deviation ≥ 64 . Based on this logic

and using quality map discussed in 4.1.2.5, NFIS assigns a quality value on the range 0.01 to

0.99 to each minutia. A low quality minutia value represents a minutia detected in a lower

quality region of the image, whereas a high quality minutia value represents a minutia detected

in a higher quality region.

For a fingerprint, NFIS detects and assesses quality of each minutia. Minutiae with quality

lower than 0.5 are not reliable. We compute the number of minutiae of quality 0.5 or better, 0.6

or better, 0.75 or better, 0.8 and better, and 0.9 and better.
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4.1.4 Feature vectors

We used the quality map and minutia quality assessment of NFIS and define our feature vectors,

as shown in Table 3. Therefore, for each fingerprint i x , an 11-dimensional feature vector vi as

listed in Table 3 is computed using MINDTCT of NFIS.

NAME DESCRIPTION

1 foreground

number of blocks that are quality 1 or better; i.e. foreground = i

i

∑U

=

4

1

where Ui is number of blocks with quality i

2 total #of minutia number of total minutiae found in the fingerprint

3 min05 number of minutiae that have quality 0.5 or better

4 min06 number of minutiae that have quality 0.6 or better

5 min075 number of minutiae that have quality 0.75 or better

6 min08 number of minutiae that have quality 0.8 or better

7 min09 number of minutiae that have quality 0.9 or better

8 quality zone 1 percentage of the foreground blocks of quality map with quality =1

9 quality zone 2 percentage of the foreground blocks of quality map with quality =2

10 quality zone 3 percentage of the foreground blocks of quality map with quality =3

11 quality zone 4 percentage of the foreground blocks of quality map with quality =4

Table 3. Feature vector description

Feature vector of the fingerprint in Figure 3 (subjectively assessed as good quality fingerprint)

is computed as:

00602_01: (1642, 57, 34, 34, 34, 34, 28, 0.053, 0.167, 0.059, 0.720)

and for the fingerprint in Figure 4 (subjectively assessed as bad quality) is:

00207_01: (1690, 182, 0, 0, 0, 0, 0, 0.168, 0.572, 0.259, 0.0006)

It is apparent that the good quality fingerprint has a large quality zone 4 and higher number of

minutia of reliability 0.9 or better where the poor quality fingerprint has a large quality zone 2

and no minutia with reliability 0.5 or better.

To get a better insight into the feature vectors and uncover its underlying structure we

performed an explanatory data analysis (eda). Eda is a graphically oriented method of data

analysis that helps to determine whether the data support the assumptions required for the

classical methods of statistical inference: an outlier-free, nearly normal distribution, and serially

uncorrelated observations. We have a set of 4 plots for each feature computed for right index

fingers of dataset DOS-C: a histogram, a box plot, a density plot, and a normal qq-plot. A

normal qq-plot (or quantile-quantile plot) consists of a plot of the ordered values of the data

versus the corresponding quantiles of a standard normal distribution. If the qq-plot is fairly
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linear, the data are reasonably Guassian, otherwise; they are not. Of these four plots, the

histogram and density plot give us the best picture of the distribution shape, while the box plot

and normal qq-plot give the clearest display of outliers. The box in each box plot represents first

and third quartile of the data, black dot inside the box represents the median, data points outside

[1st quartile, 3rd quartile] range are shown by black lines outside the box. Figure 5 summarizes

our analysis for right index fingerprints in dataset DOS-C in statistical graphics. Table 4 shows

the Spearman’s rank correlation of features of right index fingerprints of dataset DOS-C and

normalized match scores of vendor F as defined in equation 4.

QUALITY

ZONE1

QUALITY

ZONE2

QUALITY

ZONE3

QUALITY

ZONE4

NORMED

MATCH

SCORE

FOREGRO

UND

0.139 0.139 0.012 0.079 0.108

TOTAL

#OF MIN

0.258 -0.385 0.292 -0.315 0.076

MIN05 -0.347 -0.386 -0.510 0.563 0.391

MIN06 -0.349 -0.387 -0.513 0.566 0.391

MIN075 -0.412 -0.253 -0.521 0.555 0.313

MIN08 -0.398 -0.169 -0.455 0.483 0.246

MIN09 -0.339 -0.047 -0.303 0.327 0.131

NORMED

MATCH

SCORE

-0.242 -0.172 -0.255 0.289 1.0

Table 4. Features and normalized match score correlations - Dataset DOS-C – right index

fingerprints and vendor F scores. Min06 and quality zone4 have the highest correlation

(0.566). There are no significant correlations between normalized match score and

features.
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Figure 5-a. Exploratory data analysis for foreground (# of block with quality 1 or better)

Figure 5-b. Exploratory data analysis total number of minutia

Dataset DOS-C Right Index
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Figure 5-c. Exploratory data analysis for min05 (# of minutiae with quality 0.5 or better)

Figure 5-d. Exploratory data analysis for min06 (# of minutiae with quality 0.6 or better)

Dataset DOS-C Right Index
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Figure 5-e. Exploratory data analysis for min075 (# of minutiae with quality 0.75 or better)

Figure 5-f. Exploratory data analysis for min08 (# of minutiae with quality 0.8 or better)

Dataset DOS-C Right Index
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Figure 5-g. Exploratory data analysis for min09 (# of minutiae with quality 0.9 or better)

Figure 5-h. Exploratory data analysis for quality zone1 (fraction of foreground with quality 1)

Dataset DOS-C Right Index
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Figure 5-i. Exploratory data analysis for quality zone2 (fraction of foreground with quality 2)

Figure 5-j. Exploratory data analysis for quality zone3 (fraction of foreground with quality 3)

Dataset DOS-C Right Index
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Figure 5-k. Exploratory data analysis for quality zone4 (fraction of foreground with quality 4)

Figure 5-l. Exploratory data analysis for vendor F match scores

Dataset DOS-C Right Index, vendor F normalized match score
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Figure 5-m. Exploratory data analysis for vendor F non-match scores - Dataset DOS-C

Right Index

Note the departure of normal distribution for the tail of mean of non-match scores per person.

As mentioned dataset DOS-C was consolidated and therefore non-match mean is not being

artificially increased by erroneous ground truth data.

Figure 5 reveals several interesting observations:

o Outliers heavily influence data. Presence of outliers, as shown in Figures 5-a through

5-m, suggests the need for a robust statistical method. It is important to note that it will

not suffice to screen data and remove outliers because the sharp decision to keep or

reject an observation is wasteful. Also it is very difficult or even impossible to spot

outliers in multivariate data. Rejecting outliers affects the distribution. We can do better

by down-weighting dubious observations rather than by rejecting them, although we

may wish to reject completely wrong observations. To compute robust measures, we

can compute trimmed mean or rank correlation. The trimmed mean is the mean of the

central 1-2α part of the distribution, so fraction α of the observations are removed from

each end.

o The distributions are not normal, and not even nearly normal. This suggests the use of

nonparametric methods.

As shown by qq-plots of Figures 5a-m normality is not a valid assumption, so rank-based

Spearman’s ρ measures of correlation between each DOS-C right index sample’s feature and

its vendor F normalized match score (i.e. o(.) ) are computed and reported in Table 4.
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The correlation factors between features and normalized match scores are not very attractive.

Min05, min06, and quality zone 4 with 0.391, 0.391, and 0.289 respectively are the highest.

This means none of features can be a good predictor of the normalized match score all by itself.

However, jointly they might be used to predict it. To investigate any association, or correlation,

between any features and the normalized match score, scatter plots of each feature vs.

normalized match score were plotted. Scatter plots can be used as a visual check on correlation

between two variables. If two variables are strongly correlated, a scatter plot of one variable

against the other will look like a straight line. A strong correlation between any feature and

normalized match score ( o(.) ) means that we will be able to use that feature as a predictor of

normalized match score o(.) . Figures 6, 7, 8, and 9 show matrices of scatter plots of

components of feature vectors for DOS-C right index fingerprints and normalized match scores

of vendor F. Green circles represent fingerprints that are matched correctly and red ones

represent fingerprints that are not matched correctly.

Figure 6. Min06 and quality zone 4 are somehow correlated. Fingerprints with small

values of min06 and quality zone 4 are most likely identified falsely.
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Figure 7. Scatter plots min09, quality zone1, and vendor F’s normalized match score

for dataset DOS-C Right Index. Fingerprints with min09 bigger than 15 are most likely

identified correctly and so, by our definition, are of “good” quality.

Figure 8. Fingerprints with small number of min05 and large values of quality zone 3

are most likely identified falsely.
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Figure 9. Fingerprints with high number of min075 ( >20) and small values of quality

zone 2 are most likely identified correctly and hence are of “good” quality.

Scatter plots show explicitly the consequences of the feature correlation discussed in Table 4.

Figure 6 shows three sets of scatter plots between min06, quality zone4, and the normalized

match scores of vendor matcher F as generated using equation 4. The plot does not reveal a

clear relationship between either min06 or quality zone4 and normalized match scores. It does,

however, show two weak correlations. Sufficiently large values of min06 result in green circles

predicting correct match in the lower right subplot of Figure 6. Further, small values of both

quality zone4 and min06 associate incorrect match (red circles) in the center left and center

bottom subplots of Figure 6. This behavior is also seen in Figure 9 for features min075, and

quality zone2. Figure 8 shows a weak correlation between min05 and quality zone 3. Red

circles in center bottom subplot of Figure 8 indicate that sufficiently large values of quality zone

3 and small numbers of min05 result in false identification. Figure 7, for features min09 and

quality zone1, shows the correlation associated with large values of the minutia variable but do

not show a predictive effect when the minutia variable is combined with the quality variable.

Similar results hold for the effect of other features on normalized match scores.

The observation that the quality measures generated by NFIS are capable of predicting the

performance of a commercial fingerprint matching system, which uses completely different

feature extraction methods, is of great importance. This suggests that an image quality measure

based on the publicly available NFIS generated features can be used to predict matcher

performance for one and, as we will show later in section 5, other commercial systems.

To summarize, correlation factors of none of the features is strong enough to predict the

normalized match score all by itself. However, there is strong evidence that some linear or
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nonlinear combination of these features can predict normalized match scores as defined in

equation 4. The Next section discusses how these features are used to predict normalized match

scores.

4.2 Pattern recognition (Regression vs. Classification)

In the terminology of pattern recognition, the given examples, together with their

classifications, are known as the training set, and future cases form the test set. Our primary

measure of success is the error (or misclassification) rate. Note that we would obtain (possibly

seriously) biased estimates by re-classifying the training set, but that the error rate on a test

randomly chosen from the whole population will be an unbiased estimator. To avoid the over

fitting problem, we formed two disjoint subsets of fingerprint images drawn from datasets DOSC, DHS2-C, BEN, TXDPS, and DHS10, used one subset to fit the model (train set), and used

the other subset (test set) for assessment of the generalization error of the final chosen model.

Data set SD29[3] was used as validation set.

Equation 8 suggests use of various regression methods. We have a vector inputs v = (feature1,

feature2, ... , featuren), and want to predict the real valued output o(.) .The linear regression

model has the form

j

j

j Y = f x = β +∑β x 0 ( )

The linear model either assumes that the regression function E[Y | X ] is linear, or that the

linear model is a reasonable approximation. The β j are unknown parameters or coefficients,

and the variables j x can come from different sources: quantitative inputs, transformations of

quantitative inputs, such as log or square, and basis expansions, such as 2

j x , or interactions

between variables i j x x . No matter the source of j x , the model is linear in the parameters, β j .

Linear regression is a natural choice when graphical evidence clearly indicates a linear

relationship between response (normalized match score) and predictors (features). According to

scatter plots of dataset DOS-C (Figures 5-a through 5-m) and matrix plots of Figures 6, 7, 8, and

9, such a linear relationship does not exist. We tried several regression methods but none fit the

data. R2

 was never larger that 0.30, furthermore residuals did not have a standard covariance

structure and residuals had a larger spread than fitted values. Therefore, though simple, the

traditional linear model is not a good fit for our data because the classical linear regression

techniques make several strong assumptions about the underlying data, and the feature data fail

to satisfy these assumptions in several ways. For example, one or more outliers may throw off

the regression line, or the data may not have a normal distribution. We know that outliers

influence our data heavily and it does not have a normal distribution. To overcome the influence

of outliers, we used robust regression methods; these minimize the effect of outliers while

retaining the basic form of the linear method. We even replaced one or more predictors by

polynomial smooth function of the predictor, converting the classical linear model into a
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generalized additive model (GAM). In the regression setting, a generalized additive model has

the form [23]:

= α + ( ) + ( ) + + ( ) + ε 1 1 2 2 p p Y f x f x Κ f x

As usual, p x , x ,..., x 1 2 represent predictors (features) and Y is the outcome (normalized match

score); the i f ’s are unspecified smooth (and so nonparametric) functions. The approach taken

was to fit each function using a scatter plot smoother (e.g., a loess smoother or spline smoother),

and provide an algorithm for simultaneously estimating all p functions. The model was fit using

the local scoring algorithm, which iteratively fits weighted additive models by backfitting. The

backfitting algorithm is a Gauss-Seidel method for fitting additive models, by iteratively

smoothing partial residuals. The algorithm separates the parametric part from the nonparametric

part of the fit, and fits the parametric part using weighted linear least squares within the

backfitting algorithm. We tried both loess and spline smoother for the model. GAM takes care

of non-linearity and non-normality of errors (residuals) but failed due to an interaction between

predictors, j x (i.e. features), which, in turn, suggests the use of projection pursuit regression [4].

This is a non-parametric regression model that includes the possibility of having interactions

between the explanatory variables. Project pursuit regression applies an additive model to

projected variables. That is, it is of the form:

= α +∑ α + ε =( )

1

Y 0 f XT

j

M

j

j

for vectors αj, and a dimension M to be chosen by the user. Thus it uses an additive model on

predictor variables that are formed by projecting X in M carefully chosen directions. Projection

pursuit regression did not do better than any other model we discussed before.

Failure of regression and additive models, plus our rationale for choosing discrete quality

(section 3.1), leads us to state the problem as a classification problem. To investigate the

discriminatory power of our feature vectors, for right index fingerprints in data set DOS-C, we

made box plots of each feature for different levels (bins as given in Table 2) of normalized

match scores of vendor F for those correctly identified and those that are falsely identified

separately. These box plots are shown in Figure 10. The box in each box plot shows the middle

half of the data and the black dot inside the box represents the median. The whiskers are at

1.5(inter-quartile range) and data points beyond that are considered outliers and are drawn

individually. Box plots show not only the location and spread of the data but indicate skewness

as well. Trellis plots of components of feature vectors of right index fingerprints of dataset

DOS-C and normalized match scores of vendor F are in appendix B.

As it appears in Figure 10, the median of each feature varies over different performance bins.

This implies that these features can be used to partition the space of normalized match scores

o(.) as defined in equation 4.
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Figure 10. Box plots of each of our 9 features of dataset DOS-C Right index fingerprints for

different bins of normalized match scores of vendor F. The median of each feature varies over

different score bins. All samples in good, very good, and excellent bins are matched correctly.

We chose artificial neural network as the nonlinear classification method. The neural network

has the capability of acting as an approximation function for an arbitrary nonlinear function and

is not dependent on model based distribution functions in either the feature or the classification

space.

4.2.1 Training the neural network

We used neural network implementation in NFIS package. The theory behind the machine

learning techniques used in this program is discussed in [17]. It trains a 3-layer feed forward

nonlinear perceptron model. The input nodes are feed-forwardly connected to the hidden nodes,

and the hidden nodes to the output nodes. Training (optimization) of the weights is done using

either a scaled conjugate gradient algorithm [18], or by starting out with scaled conjugate

gradient and then switching to a limited memory Broyden Fletcher Goldfarb Shanno algorithm

[19]. Boltzmann pruning [20], i.e. dynamic removal of connections, is performed during

training.

In section 3.1 (Table 2) we explained our 5 classes of quality number. These 5 classes are our

output of neural network. Input to neural network is 11-dimension feature vector discussed in

section 4.1.4. We chose 22 hidden nodes. The activation functions used for hidden nodes as

well as output nodes are sinusoid. Boltzmann pruning was set.
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Our training set consists of plain right index, plain left index, plain right thumb, and plain left

thumb fingerprints from datasets DOS-C, DHS2-C, DHS10, TXDPS, and BEN. As explained

in section 1.2, these datasets contain fingerprints collected by different personnel, at different

locales, and under different conditions. Therefore the training set contains various levels of

quality. We have tested our system on VISIT_POE and VISIT_POE_BVA datasets and a subset

of DOS-C, DHS2-C, DHS10, TXDPS, and BEN that was not used for training.

A full similarity matrix of the training set is needed for training the neural network. Similarity

scores are needed to compute the output class of neural network (i.e. bins of normalized match

scores). Since bin boundaries are based on distribution of match and non-match scores (Table

2), one may argue that training based on similarity scores of one vendor will result in “vendor

dependent” weights and so the whole system may be biased and not generalize to other

matchers. To avoid such a problem, we have chosen samples that are assigned the same bins by

SDK vendors F, G, and VTB. Therefore, similarity scores of SDK vendors F, G, and VTB were

used for our training.

Care was taken to design a training set balanced in terms of numbers of different finger

positions of different quality of different datasets. Table 5 summarizes the diversity of our

training set.

RIGHT INDEX LEFT INDEX RIGHT THUMB LEFT THUMB

Quality 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 45

DOS_C 100 100 83 21 31 100 100 69 18 62

DHS2_C 100 100 90 29 21 100 100 79 11 30

BEN 40 40 40 40 26 40 40 40 40 40 60 60 60 60 7 60 60 60 60 15

TXDPS 40 40 40 38 35 40 40 40 36 34 60 60 60 43 14 60 60 60 31 18

DHS10 20 20 20 20 20 20 20 20 20 20 80 80 80 32 32 80 80 80 36 39

TOTAL 300 300 273 148 133 300 300 248 125 186 200 200 200 135 53 200 200 200 127 72

Table 5. Training set structure: Number of samples. Note the totals from each class are:

1000, 1000, 421, 535, and 444.

In an ideal case, we want our training set to have equal numbers of each fingers (right/left

index/thumb) with equal numbers of different quality, e.g. 300 right index fingerprints of each

quality 1, 2, 3, 4, and 5, and same for left index and right / left thumb. Plus it is preferred to

have equal number of samples from each above-mentioned datasets. However, in reality only a

small fraction of each dataset is of very poor quality, and so our training set has more samples

of high and good quality than samples of low and very low quality. This is common in

classification problems where large samples of rare classes may not be available. We have

compensated for lower samples of poor quality in our training set by setting “class-weight”
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parameter of our implementation of neural network [17]. Our implementation of neural network

accepts class prior probabilities as a training parameter. In the scaled conjugate gradient method

used here, both the network errors and the error signals used in the training are calculated using

class weights. This ensures that the optimization is performed in a way that produces the best

solution to the global problem but allows reasonable sampling of less common classes.

For each training sample, the class of neural net output is determined based on its match and

non-match scores, which is computed for a pair of (probe, gallery). We know that the lower

match score (and/or higher non-match mean) is due to the lower quality of the pair. However,

prior to training, it is not obvious which of the (probe, gallery) fingerprint images is of the lower

quality and so both probe and gallery fingerprint images are assigned the same class. If the

quality of the probe and gallery are the same, samples are labeled correctly and so the neural

network is trained correctly. However, if the quality of the probe and gallery are not the same,

the better quality image of the pair (probe, gallery) is labeled with the wrong class. For example

a probe image with very low number of good quality minutia and small quality zone 4

(indicators of low quality), scored against a gallery image with high number of good quality

minutia and large quality zone 4 (indicators of good quality), will result in a low normalized

match score (eq. 4). Both probe and gallery will be assigned the same class (which is low

quality) and so the gallery image (the better quality image of the pair) is obviously labeled with

the wrong class. We solved this problem by adjusting the “pattern-weights” parameter of our

implementation of the neural network. The pattern-weights are used in the calculation of the

error value that neural network attempts to minimize during training. When the training patterns

(features) are sent through the network, each pattern produces an error contribution, which gets

multiplied by the pattern-weight for that pattern before added to an error accumulator.

In our case, to assure that the neural network is trained properly, we must assign a lower weight

to the better quality image of the pair (probe, gallery) for each subject. As discussed above,

there is no knowledge of relative quality of the pair (probe, gallery) prior to training. However,

we can use the predicted class of quality for each image after training to adjust pattern-weights

and then repeat the training with adjusted pattern-weights. Therefore, we performed two rounds

of training: First, we trained the neural network giving equal weights (all weights = 1) to all

feature vectors. Upon completion of training, neural network predicts class of quality for each

image in the training set. New pattern-weights are assigned to each image based on this

predicted quality for the two images (probe, gallery) of each subject, and training is repeated

using the new and adjusted pattern-weights. Pattern-weights are adjusted as explained below:

For each subject (person) in training set,

If qprobe = qgallery , set probe’s pattern-weight = 0.5 and gallery’s pattern-weight = 0.5

If qprobe < qgallery , set probe’s pattern-weight = 0 and gallery’s pattern-weight = 1

If qprobe > qgallery , set probe’s pattern-weight = 1 and gallery’s pattern-weight = 0

Note that quality=5 is the poorest quality and quality=1 is the best quality.

To make sure that all feature vectors fed to the neural network are in the same range, feature

vectors are normalized by vectors of global mean and global standard deviation of all feature

vectors of datasets DOS-C, DHS2-C, BEN, DHS10, and TXDPS.
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We have used similarity scores of vendor A Æ L, N, and VTB of the SDK test performed at

NIST [6] to test our proposed fingerprint image quality metrics. Results of vendor F for dataset

VISIT_POE have been reported in the body of this report, and results for all the other vendors

for all other datasets (a total of 280 ROCs) are in appendix A. It should be noted that no subset

of the VISIT_POE dataset has been used in training the neural network.

5 Assessing Fingerprint Image Quality

It is difficult to obtain the fingerprint image quality benchmark to test the performance of a

quality analysis algorithm. The most used method to test image quality measures are based on

visual (and thus subjective) assessments of images. However, with growing size of databases,

manual judgment is extremely time-consuming and expensive. The present complete NIST

fingerprint test archive consists of approximately 68 million fingerprints from 16 million

individuals. Thus, objective measures that correlate acceptably well with perceived image

quality and/or matcher performance are essential.

Our evaluation criterion is rank ROC as a function of image quality. We measure quality

number q for probe and gallery fingerprint pairs of each person in our test datasets, and quality

is defined by the minimum of the two numbers. Given a quality number taking on K integral

values, K ROC characteristics are generated. If the k-th quality number is qk , then the similarity

scores used in the computation of the K non-match distributions N (s) k and K match

distributions M (s) k are those for which the image pair has quality qk by equations 6 and 8.

The selection of number K is a design issue. At minimum K=2, that is a binary decision to

accept the image as good quality or reject the image as poor quality is made. We have chosen K

= 5. The choice of 5 levels of image quality is a compromise. Eight to ten levels would be

needed to fully characterize a matcher that is very sensitive to image quality, such as SDK

matcher A [6]. Matchers that are insensitive to image quality, such as SDK matchers H and I,

could be well characterized by three levels of image quality. We have selected five levels which

is the correct level for a medium to good quality matcher such as SDK matcher F.

Figures 11-a and 11-b show rank ROC performance as a function of our quality analysis for

right and left index fingers of dataset VISIT_POE and similarity scores of SDK vendor F. These

figures show that our proposed image quality works as a rank statistic for performance. It is

worth mentioning that no subset of the VISIT_POE data has been used in training the neural

network.

Each of the figures showing results, (Figures 11-24), consists of three sections. The first section

of the figures contains a ranked set of ROC curves for image quality levels 1-5. To study the

difference in performance for different levels of quality, it is necessary to combine results over

the set of 5 ROCs (one for each class of quality). One method of combining results is to

measure the variation of the TAR for each FAR. This requires readjusting the operating

threshold for each probe set (i.e. different levels of quality). For many applications, this is not

feasible or desirable. However, it is an appropriate technique for comparing performance of
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different systems because it is not possible to set uniform thresholds across different systems.

To study the effect of quality on the performance of a system, we have to set one operating

threshold across all galleries and probe sets. Fixing the threshold across various levels of quality

corresponds to an operational situation in which threshold is set once for an application. The

operating-threshold is picked based upon a specific false accept rate (usually 1%) on an

aggregated population. The second section of each figure contains a table showing operatingthreshold and its corresponding FAR and TAR values for the aggregated population as well as

FAR and TAR values for each image quality level computed at operating-threshold. The third

section of each figure contains a plot of the values given in the second section. Specifically, it

show false accept rate and true accept rate of probe sets of different quality at the fixed

operating-threshold. The red square marks the false accept rate and true accept rate of the whole

dataset at the fixed operating-threshold.
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Vendor F – VISIT_POE – Right index

threshold=350 (far,tar)=(0.0119418,0.991333)

quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0037 0.0083 0.0131 0.0216 0.0477

TAR 0.997 0.994 0.993 0.9496 0.926

Figure 11-a. The effect of quality on ROC - Vendor F - Dataset VISIT_POE - Right index
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VendorF –VISIT_POE Left Index

threshold=350 (far,tar)=(0.0120981,0.989)

quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0043 0.0073 0.0124 0.0228 0.0573

TAR 0.997 0.998 0.993 0.953 0.856

Figure 11-b. The effect of quality on ROC - Vendor F - Dataset VISIT_POE - Left index
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A desirable feature of a quality measure is how well it generalizes to other matchers and other

datasets. To investigate how well our system generalizes to other matchers, we have used

similarity scores of SDK vendors A Æ L, N, and VTB to test our proposed fingerprint image

quality metrics on all datasets explained in 1.2. Results of dataset VISIT_POE are shown in

Figures 11 through 24, where results of all SDK vendors (14 vendors) and all datasets (20

datasets) - - a total of 280 ROCs -- are in appendix A. These ROCs show our fingerprint quality

measure generalizes very well to other matchers and other datasets.

The table and plot of (false accept rate, true accept rate) pair for each quality at the same

operating point, i.e. fixed threshold, follow each ROC. The threshold and (false accept rate, true

accept rate) of the corresponding dataset is reported as well. The red square marks the false

accept rate and true accept rate of the whole dataset at the operation-threshold specified in the

table in the center section of each figure. As quality degrades, we expect lower performance, i.e.

lower true accept rate and/or higher false accept rate. Figures 11 through 24 show for vendors F,

C, G, I, and J the false accept rate and true accept rate of quality 1 and 2 (excellent and very

good) fingerprints are almost always on the left side of the red square. It means matcher

performance for probe sets with quality 1 (excellent) and quality 2 (very good) is better than the

whole dataset. False accept rates and true accept rates of quality 3, 4, 5 (good, fair, and poor)

are usually on the right side of the red square, which indicates lower true accept rate AND

higher false accept rate than the whole dataset.

As quality degrades, true accept rate decreases for all the matchers. It is also desirable to see an

increase in FAR as quality improves. Vendors F, C, G, J, and I exhibit such a behavior: as

quality degrades, a lower TAR and a higher FAR is observed. Vendors A, B, N, and VTB’s

false accept rate decreases as quality degrades. There is no pattern for vendors D, E, H, K, and

L’s far as quality varies. Increasing false accept rate as quality degrades indicates vulnerability

of low quality fingerprint images.

There are several interesting trends in vendor matcher performance sensitivity to image quality

as defined in this paper. When all vendors are examined on quality level one data, only a few of

them can get TARs over 99.6%. This suggests that there are error sources that generate

approximately 0.4% errors that are independent of image quality as defined in this paper.

At the same time the FAR of different vendors at the operating-threshold ranges from 2.8% for

vendor N to 0.02% for vendor I. This shows that the FAR of systems with relatively low

accuracy rank in the SDK tests is not easily correlated with the TAR of the system.

In most cases, where the FAR of a vendor for quality 1 fingerprints is below 0.1%, the vendor

has very uniform response to fingerprints of quality 1, 2, and 3. This demonstrates that very

high accuracy systems need fewer than five levels of image quality to fully characterize their

performance.

Finally, examination of the results of the SDK report shows that for the top five systems, SDK

systems H, I, J, F, and G, the image quality 3 TAR on VISIT_POE data is a good overall

predictor of SDK rank. The ranks of the various SDK vendor systems on different datasets are

given in Table 7 taken from [6]. The ranks of the top five vendors, based on quality level 3 TAR

at the fixed operating-threshold, are given in Table 8. The ranks between the DOS-C and the
NIST Fingerprint Image Quality Page 42 08/19/04

DHS2-C index finger data set and the quality 2 TARs differ only in the placement of systems H

and F. This difference between this ranking and the other SDK datasets is in part due to the flat

to rolled matching tested by BEN, DHS10, TXDPS, OHIO, and SD29 datasets.

DOS-C/DHS2-C BEN/DHS10/TXDPS ALL

System I System H System H

System H System I System I

System F System J System J

System J System F System F

System G System G System G

System D System D System D

System C System K System C

System K System C System K

System L System L System L

System B System VTB System A

System VTB System A System VTB

System A System B System B

System E System E System E

Table 6. SDK rankings at FAR 0.01% for the different datasets taken from [6]
NIST Fingerprint Image Quality Page 43 08/19/04

QUALITY3

LEFT

INDEX

RANK

VISIT_POE

RIGHT

INDEX

TAR

VISIT_POE

LEFT

INDEX

TAR

System I 0.993 0.996

System F 0.993 0.993

System H 0.993 0.995

System J 0.986 0.990

System G 0.984 0.988

Table 7. Image quality rankings for image quality 3 TAR using the left index fingerprints from

US_VISIT_POE datasets for different vendors. The threshold and TAR values are taken from

figure 11 for system F and from figures 17- 20 for other systems.

Distribution of qualities in the VISIT_POE dataset is shown in Table 8. Distribution of datasets

DOS-C, DHS2-C, DHS10, BEN, TXDPS, and VISIT_POE_BVA are in appendix A.

Quality

#of

sample

in test set

%of

total

1 (excellent) 1637 27.28

2 (very good) 1999 33.32

3 (good) 2122 35.37

4 (fair) 134 2.23 RIGHT INDEX

5 (poor) 108 1.8

1 (excellent) 1850 30.83

2 (very good) 1744 29.07

3 (good) 2087 34.78

4 (fair) 173 2.88

LEFT INDEX

5 (poor) 146 2.43

0

5

10

15

20

25

30

35

40

q1 q2 q3 q4 q5

VISIT_POE Right Inex% VISIT_POE Left Index%

Table 8. Percentage of various quality levels for dataset VISIT_POE
NIST Fingerprint Image Quality Page 44 08/19/04

Vendor A– VISIT_POE – Right index

threshold=0.24 (far,tar)=(0.0144008,0.962833)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0170 0.0146 0.0142 0.0096 0.0081

TAR 0.988 0.981 0.967 0.832 0.620
NIST Fingerprint Image Quality Page 45 08/19/04

Figure 12-a. The effect of quality on ROC - Vendor A - Dataset VISIT_POE - Right index

Vendor A – VISIT_POE – Left index

threshold=0.26 (far,tar)=(0.0126554,0.9375)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0159 0.013 0.012 0.0071 0.0059

TAR 0.985 0.981 0.940 0.689 0.534
NIST Fingerprint Image Quality Page 46 08/19/04

Figure 12-b. The effect of quality on ROC - Vendor A - Dataset VISIT_POE - Left index

Vendor B – VISIT_POE – Left index

threshold=124 (far,tar)=(0.0102684,0.941667)

Quality 1(excellent) 2(very

good) 3(good) 4(fair) 5(poor)

FAR 0.0187 0.0088 0.0100 0.0067 0.0030
NIST Fingerprint Image Quality Page 47 08/19/04

TAR 0.996 0.987 0.949 0.722 0.493

Figure 13-a. The effect of quality on ROC - Vendor B - Dataset VISIT_POE Left index

Vendor C – VISIT_POE – Left index

threshold=110 (far,tar)=(0.001,0.975)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.00066 0.00045 0.00099 0.00225 0.00511
NIST Fingerprint Image Quality Page 48 08/19/04

TAR 0.997 0.993 0.976 0.867 0.781

Figure 14-a. The effect of quality on ROC - Vendor C - Dataset VISIT_POE - Left index

Vendor C – VISIT_POE – Right index

 threshold=50 (far,tar)=(0.001,0.982)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.00067 0.00052 0.00114 0.00261 0.00624
NIST Fingerprint Image Quality Page 49 08/19/04

TAR 0.995 0.991 0.983 0.899 0.880

Figure 14-b. The effect of quality on ROC - Vendor C - Dataset VISIT_POE -Right index

Vendor D – VISIT_POE – Left index

threshold=600 (far,tar)=(0.016,0.984)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0179 0.0206 0.0145 0.0094 0.0150

TAR 0.997 0.998 0.992 0.907 0.774
NIST Fingerprint Image Quality Page 50 08/19/04

Figure 15-a. The effect of quality on ROC - Vendor D - Dataset VISIT_POE - Left index

Vendor D – VISIT_POE – Right index

threshold=650 (far,tar)=(0.01,0.986)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.010 0.013 0.0096 0.006 0.0091

TAR 0.997 0.994 0.990 0.924 0.8426
NIST Fingerprint Image Quality Page 51 08/19/04

Figure 15-b. The effect of quality on ROC - Vendor D - Dataset VISIT_POE - Right index

Vendor E – VISIT_POE – Right index

threshold=60 (far,tar)=( 0.018,0.828)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0384 0.0145 0.0172 0.0206 0.0059

TAR 0.988 0.935 0.749 0.403 0.194
NIST Fingerprint Image Quality Page 52 08/19/04

Figure 16-a. The effect of quality on ROC - Vendor E - Dataset VISIT_POE - Right index

Vendor E – VISIT_POE – Left index

threshold=60 (far,tar)=( 0.037,0.829)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.075 0.030 0.034 0.031 0.01

TAR 0.990 0.946 0.764 0.298 0.164
NIST Fingerprint Image Quality Page 53 08/19/04

Figure 16-b. The effect of quality on ROC - Vendor E - Dataset VISIT_POE - Left index

Vendor G – VISIT_POE – Left index

threshold=2700 (far,tar)=(0.012,0.980)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0099 0.0102 0.0140 0.0159 0.0172

TAR 0.996 0.994 0.984 0.934 0.801
NIST Fingerprint Image Quality Page 54 08/19/04

Figure 17-a. The effect of quality on ROC - Vendor G - Dataset VISIT_POE - Left index

Vendor G – VISIT_POE – Right index

 threshold=2700 (far,tar)=(0.011,0.985)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0084 0.0086 0.0128 0.0147 0.0161

TAR 0.996 0.991 0.988 0.941 0.824
NIST Fingerprint Image Quality Page 55 08/19/04

Figure 17-b. The effect of quality on ROC - Vendor G - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 56 08/19/04

Figure 18-a. The effect of quality on ROC - Vendor H - Dataset VISIT_POE - Left index

Vendor H – VISIT_POE – Left index

 threshold=40 (far,tar)=(0.001,0.991)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0017 0.0008 0.0014 0.0017 0.0013

TAR 0.997 0.998 0.995 0.974 0.870
NIST Fingerprint Image Quality Page 57 08/19/04

Vendor H – VISIT_POE – Right index

 threshold=30 (far,tar)=(0.001,0.992)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0016 0.00080 0.00129 0.00164 0.00183

TAR 0.996 0.995 0.993 0.975 0.917

Figure 18-b. The effect of quality on ROC - Vendor H - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 58 08/19/04

Vendor I – VISIT_POE – Left index

threshold=400 (far,tar)=( 0.001,0.992)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.00058 0.00101 0.00191 0.00383 0.01129

TAR 0.998 0.997 0.996 0.972 0.880

Figure 19-a. The effect of quality on ROC - Vendor I - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 59 08/19/04

Vendor I – VISIT_POE – Right index

threshold=450 (far,tar)=(0.001,0.99)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.00022 0.00069 0.00133 0.00336 0.00789

TAR 0.997 0.995 0.993 0.933 0.889

Figure 19-b. The effect of quality on ROC - Vendor I - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 60 08/19/04

Vendor J – VISIT_POE – Left index

threshold=2600 (far,tar)=(0.01,0.987)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0094 0.0101 0.0114 0.0118 0.0181

TAR 0.996 0.997 0.990 0.954 0.877

Figure 20-a. The effect of quality on ROC - Vendor J - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 61 08/19/04

Vendor J – VISIT_POE – Right index

threshold=2600 (far,tar)=(0.01,0.987)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0082 0.0088 0.0108 0.0111 0.0170

TAR 0.997 0.992 0.986 0.958 0.870

Figure 20-b. The effect of quality on ROC - Vendor J - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 62 08/19/04

Vendor K – VISIT_POE – Left index

 threshold=14 (far,tar)=(0.011,0.969)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0136 0.0114 0.0106 0.0076 0.0147

TAR 0.995 0.994 0.978 0.874 0.651

Figure 21-a. The effect of quality on ROC - Vendor K - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 63 08/19/04

Vendor K – VISIT_POE – Right index

threshold=12 (far,tar)=(0.018,0.980)

quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0199 0.0175 0.0176 0.0143 0.0210

TAR 0.994 0.990 0.986 0.924 0.778

Figure 21-b. The effect of quality on ROC - Vendor K - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 64 08/19/04

Vendor L – VISIT_POE – Left index

threshold=2100 (far,tar)=(0.01,0.952)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0102 0.0151 0.0090 0.0075 0.0065

TAR 0.997 0.994 0.958 0.722 0.514

Figure 22-a. The effect of quality on ROC - Vendor L - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 65 08/19/04

Vendor L – VISIT_POE – Right index

threshold=2000 (far,tar)=(0.012,0.962)

Quality 1(excellent) 2(very

good) 3(good) 4(fair) 5(poor)

FAR 0.0101 0.0157 0.0104 0.0087 0.0073

TAR 0.992 0.987 0.961 0.807 0.602

Figure 22-b. The effect of quality on ROC - Vendor L - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 66 08/19/04

Vendor N – VISIT_POE – Left index

threshold=36 (far,tar)=(0.01,0.979)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0234 0.0096 0.0088 0.0062 0.0028

TAR 0.997 0.992 0.981 0.914 0.815

Figure 23-a. The effect of quality on ROC - Vendor N - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 67 08/19/04

Vendor N – VISIT_POE – Right index

threshold=34 (far,tar)=(0.01,0.984)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0287 0.0113 0.0099 0.0086 0.0036

TAR 0.994 0.989 0.984 0.949 0.861

Figure 23-b. The effect of quality on ROC - Vendor N - Dataset VISIT_POE - Right index
NIST Fingerprint Image Quality Page 68 08/19/04

Vendor VTB – VISIT_POE – Left index

threshold=18 (far,tar)=(0.011,0.916)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.0191 0.0168 0.0087 0.0040 0.0028

TAR 0.991 0.980 0.905 0.623 0.397

Figure 24-a. The effect of quality on ROC - Vendor VTB - Dataset VISIT_POE - Left index
NIST Fingerprint Image Quality Page 69 08/19/04

Vendor VTB – VISIT_POE – Right index

threshold=18 (far,tar)=(0.01,0.93)

Quality 1(excellent) 2(very good) 3(good) 4(fair) 5(poor)

FAR 0.019 0.0138 0.0085 0.0039 0.0020

TAR 0.989 0.972 0.918 0.714 0.435

Figure 24-b. The effect of quality on ROC - Vendor VTB - Dataset VISIT_POE - Right index

6 Conclusion

In this report we developed a method to assess quality of a fingerprint that can forecast matcher

performance. This includes an objective method of evaluating quality of fingerprints. Our

definition of quality can be applied to other biometric modalities and upon proper feature

extraction can be used to assess quality of any mode of biometric samples.

This paper explains the method used to generate quality determining feature vectors from the

NIST NFIS fingerprint software. The method used to train an artificial neural network that

generates image quality values is then presented. These image quality values were then tested

on 300 different combinations of fingerprint images data and fingerprint matcher systems and

found to predict matcher performance for all systems and datasets. The test results presented in

the body of the report for US-VISIT POE data show that the method is highly accurate even for

matcher and data combinations that were not used in the neural network training.

Automatically and consistently determining quality of a given biometric sample for

identification and/or verification is a problem with far reaching applications. If one can

determine low quality biometric samples, this information can be used to improve the

acquisition of new data. This same quality measure can also be used to selectively improve

archival biometric gallery by replacing poor quality biometric samples with better quality

samples. Weights for multimodal biometric fusion can be selected to allow better quality

biometric samples to dominate the fusion. All of these applications require that the quality of
NIST Fingerprint Image Quality Page 70 08/19/04

the biometric sample be determined prior to identification or verification. The image quality

measure presented here meets this requirement. Most of these applications also require that

quality of the biometric sample be computed in real time during data acquisition. The method

presented here is fast enough to meet this speed requirement.
NIST Fingerprint Image Quality Page 71 08/19/04

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Appendix A

ROCs

Data set quality distributions

This appendix is in a separate file.

Appendix B

Trellis plots of feature vector components and performance

This appendix is in a separate file.